Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations20000
Missing cells8261
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory128.0 B

Variable types

Numeric10
Text3
Categorical2
DateTime1

Alerts

host_id is highly overall correlated with idHigh correlation
id is highly overall correlated with host_idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
last_review has 4123 (20.6%) missing values Missing
reviews_per_month has 4123 (20.6%) missing values Missing
minimum_nights is highly skewed (γ1 = 25.17996962) Skewed
id has unique values Unique
number_of_reviews has 4123 (20.6%) zeros Zeros
availability_365 has 7176 (35.9%) zeros Zeros

Reproduction

Analysis started2025-11-05 14:18:00.956988
Analysis finished2025-11-05 14:18:13.553117
Duration12.6 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18923801
Minimum2539
Maximum36485609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:13.642467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1193873.9
Q19393540.5
median19521168
Q329129359
95-th percentile35275607
Maximum36485609
Range36483070
Interquartile range (IQR)19735818

Descriptive statistics

Standard deviation11012232
Coefficient of variation (CV)0.58192498
Kurtosis-1.233323
Mean18923801
Median Absolute Deviation (MAD)9896304
Skewness-0.075380527
Sum3.7847602 × 1011
Variance1.2126926 × 1014
MonotonicityNot monotonic
2025-11-05T15:18:13.780430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6311716 1
 
< 0.1%
8205572 1
 
< 0.1%
34483432 1
 
< 0.1%
18486868 1
 
< 0.1%
7322088 1
 
< 0.1%
26893534 1
 
< 0.1%
9170129 1
 
< 0.1%
20724736 1
 
< 0.1%
5414471 1
 
< 0.1%
9833212 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5121 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
5803 1
< 0.1%
6090 1
< 0.1%
6848 1
< 0.1%
7750 1
< 0.1%
ValueCountFrequency (%)
36485609 1
< 0.1%
36485057 1
< 0.1%
36480292 1
< 0.1%
36479723 1
< 0.1%
36478343 1
< 0.1%
36472171 1
< 0.1%
36471896 1
< 0.1%
36468880 1
< 0.1%
36458668 1
< 0.1%
36456829 1
< 0.1%

name
Text

Distinct19768
Distinct (%)98.9%
Missing7
Missing (%)< 0.1%
Memory size156.4 KiB
2025-11-05T15:18:14.038260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length179
Median length68
Mean length36.902466
Min length1

Characters and Unicode

Total characters737791
Distinct characters511
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19599 ?
Unique (%)98.0%

Sample

1st rowPrivate Lg Room 15 min to Manhattan
2nd rowTIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC
3rd rowVoted #1 Location Quintessential 1BR W Village Apt
4th rowSpacious 1 bedroom apartment 15min from Manhattan
5th rowBig beautiful bedroom in huge Bushwick apartment
ValueCountFrequency (%)
in 6813
 
5.6%
room 4038
 
3.3%
3388
 
2.8%
bedroom 3145
 
2.6%
private 2935
 
2.4%
apartment 2748
 
2.2%
cozy 2034
 
1.7%
apt 1843
 
1.5%
brooklyn 1666
 
1.4%
the 1620
 
1.3%
Other values (7025) 91941
75.3%
2025-11-05T15:18:14.391250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102878
 
13.9%
e 50869
 
6.9%
o 50103
 
6.8%
t 42994
 
5.8%
a 42392
 
5.7%
r 40165
 
5.4%
i 38708
 
5.2%
n 38494
 
5.2%
l 21159
 
2.9%
m 20160
 
2.7%
Other values (501) 289869
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 737791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
102878
 
13.9%
e 50869
 
6.9%
o 50103
 
6.8%
t 42994
 
5.8%
a 42392
 
5.7%
r 40165
 
5.4%
i 38708
 
5.2%
n 38494
 
5.2%
l 21159
 
2.9%
m 20160
 
2.7%
Other values (501) 289869
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 737791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
102878
 
13.9%
e 50869
 
6.9%
o 50103
 
6.8%
t 42994
 
5.8%
a 42392
 
5.7%
r 40165
 
5.4%
i 38708
 
5.2%
n 38494
 
5.2%
l 21159
 
2.9%
m 20160
 
2.7%
Other values (501) 289869
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 737791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
102878
 
13.9%
e 50869
 
6.9%
o 50103
 
6.8%
t 42994
 
5.8%
a 42392
 
5.7%
r 40165
 
5.4%
i 38708
 
5.2%
n 38494
 
5.2%
l 21159
 
2.9%
m 20160
 
2.7%
Other values (501) 289869
39.3%

host_id
Real number (ℝ)

High correlation 

Distinct17027
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67460344
Minimum2571
Maximum2.7427328 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:14.526609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2571
5-th percentile779450
Q17853718.2
median31114310
Q31.0684256 × 108
95-th percentile2.4209584 × 108
Maximum2.7427328 × 108
Range2.7427071 × 108
Interquartile range (IQR)98988842

Descriptive statistics

Standard deviation78579365
Coefficient of variation (CV)1.1648231
Kurtosis0.20879863
Mean67460344
Median Absolute Deviation (MAD)27863860
Skewness1.219649
Sum1.3492069 × 1012
Variance6.1747166 × 1015
MonotonicityNot monotonic
2025-11-05T15:18:14.666979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 131
 
0.7%
107434423 89
 
0.4%
30283594 53
 
0.3%
12243051 37
 
0.2%
137358866 36
 
0.2%
16098958 36
 
0.2%
61391963 34
 
0.2%
22541573 32
 
0.2%
200380610 27
 
0.1%
26377263 24
 
0.1%
Other values (17017) 19501
97.5%
ValueCountFrequency (%)
2571 1
 
< 0.1%
2787 3
< 0.1%
3151 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
3647 2
< 0.1%
4396 1
 
< 0.1%
4869 1
 
< 0.1%
5089 1
 
< 0.1%
6041 1
 
< 0.1%
ValueCountFrequency (%)
274273284 1
< 0.1%
274195458 1
< 0.1%
274103383 1
< 0.1%
274079964 1
< 0.1%
273870123 1
< 0.1%
273841667 1
< 0.1%
273812306 1
< 0.1%
273741577 1
< 0.1%
273656890 1
< 0.1%
273632292 1
< 0.1%
Distinct6517
Distinct (%)32.6%
Missing8
Missing (%)< 0.1%
Memory size156.4 KiB
2025-11-05T15:18:14.834130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length35
Median length31
Mean length6.112445
Min length1

Characters and Unicode

Total characters122200
Distinct characters140
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4203 ?
Unique (%)21.0%

Sample

1st rowIris
2nd rowJohlex
3rd rowJohn
4th rowRegan
5th rowMegan
ValueCountFrequency (%)
428
 
1.9%
and 250
 
1.1%
david 185
 
0.8%
michael 183
 
0.8%
sonder 168
 
0.8%
john 148
 
0.7%
nyc 137
 
0.6%
alex 129
 
0.6%
laura 121
 
0.5%
maria 109
 
0.5%
Other values (6048) 20426
91.7%
2025-11-05T15:18:15.165184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 15469
 
12.7%
e 11787
 
9.6%
i 9892
 
8.1%
n 9892
 
8.1%
r 7360
 
6.0%
l 6239
 
5.1%
o 5194
 
4.3%
t 3817
 
3.1%
s 3737
 
3.1%
h 3686
 
3.0%
Other values (130) 45127
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 15469
 
12.7%
e 11787
 
9.6%
i 9892
 
8.1%
n 9892
 
8.1%
r 7360
 
6.0%
l 6239
 
5.1%
o 5194
 
4.3%
t 3817
 
3.1%
s 3737
 
3.1%
h 3686
 
3.0%
Other values (130) 45127
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 15469
 
12.7%
e 11787
 
9.6%
i 9892
 
8.1%
n 9892
 
8.1%
r 7360
 
6.0%
l 6239
 
5.1%
o 5194
 
4.3%
t 3817
 
3.1%
s 3737
 
3.1%
h 3686
 
3.0%
Other values (130) 45127
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 15469
 
12.7%
e 11787
 
9.6%
i 9892
 
8.1%
n 9892
 
8.1%
r 7360
 
6.0%
l 6239
 
5.1%
o 5194
 
4.3%
t 3817
 
3.1%
s 3737
 
3.1%
h 3686
 
3.0%
Other values (130) 45127
36.9%

neighbourhood_group
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Manhattan
8774 
Brooklyn
8265 
Queens
2355 
Bronx
 
441
Staten Island
 
165

Length

Max length13
Median length9
Mean length8.1783
Min length5

Characters and Unicode

Total characters163566
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQueens
2nd rowManhattan
3rd rowManhattan
4th rowQueens
5th rowBrooklyn

Common Values

ValueCountFrequency (%)
Manhattan 8774
43.9%
Brooklyn 8265
41.3%
Queens 2355
 
11.8%
Bronx 441
 
2.2%
Staten Island 165
 
0.8%

Length

2025-11-05T15:18:15.295845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-05T15:18:15.423921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 8774
43.5%
brooklyn 8265
41.0%
queens 2355
 
11.7%
bronx 441
 
2.2%
staten 165
 
0.8%
island 165
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 28939
17.7%
a 26652
16.3%
t 17878
10.9%
o 16971
10.4%
h 8774
 
5.4%
M 8774
 
5.4%
B 8706
 
5.3%
r 8706
 
5.3%
l 8430
 
5.2%
k 8265
 
5.1%
Other values (10) 21471
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 163566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 28939
17.7%
a 26652
16.3%
t 17878
10.9%
o 16971
10.4%
h 8774
 
5.4%
M 8774
 
5.4%
B 8706
 
5.3%
r 8706
 
5.3%
l 8430
 
5.2%
k 8265
 
5.1%
Other values (10) 21471
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 163566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 28939
17.7%
a 26652
16.3%
t 17878
10.9%
o 16971
10.4%
h 8774
 
5.4%
M 8774
 
5.4%
B 8706
 
5.3%
r 8706
 
5.3%
l 8430
 
5.2%
k 8265
 
5.1%
Other values (10) 21471
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 163566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 28939
17.7%
a 26652
16.3%
t 17878
10.9%
o 16971
10.4%
h 8774
 
5.4%
M 8774
 
5.4%
B 8706
 
5.3%
r 8706
 
5.3%
l 8430
 
5.2%
k 8265
 
5.1%
Other values (10) 21471
13.1%
Distinct217
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:15.616183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length17
Mean length11.8819
Min length4

Characters and Unicode

Total characters237638
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st rowSunnyside
2nd rowHell's Kitchen
3rd rowWest Village
4th rowAstoria
5th rowBushwick
ValueCountFrequency (%)
east 2690
 
8.3%
side 1872
 
5.8%
williamsburg 1580
 
4.9%
harlem 1579
 
4.9%
upper 1504
 
4.7%
bedford-stuyvesant 1503
 
4.6%
heights 1462
 
4.5%
village 1301
 
4.0%
west 1142
 
3.5%
bushwick 987
 
3.1%
Other values (229) 16713
51.7%
2025-11-05T15:18:15.905545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 21824
 
9.2%
i 17188
 
7.2%
s 16285
 
6.9%
t 15839
 
6.7%
a 15504
 
6.5%
l 14091
 
5.9%
r 13837
 
5.8%
12333
 
5.2%
n 10687
 
4.5%
o 9832
 
4.1%
Other values (44) 90218
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21824
 
9.2%
i 17188
 
7.2%
s 16285
 
6.9%
t 15839
 
6.7%
a 15504
 
6.5%
l 14091
 
5.9%
r 13837
 
5.8%
12333
 
5.2%
n 10687
 
4.5%
o 9832
 
4.1%
Other values (44) 90218
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21824
 
9.2%
i 17188
 
7.2%
s 16285
 
6.9%
t 15839
 
6.7%
a 15504
 
6.5%
l 14091
 
5.9%
r 13837
 
5.8%
12333
 
5.2%
n 10687
 
4.5%
o 9832
 
4.1%
Other values (44) 90218
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21824
 
9.2%
i 17188
 
7.2%
s 16285
 
6.9%
t 15839
 
6.7%
a 15504
 
6.5%
l 14091
 
5.9%
r 13837
 
5.8%
12333
 
5.2%
n 10687
 
4.5%
o 9832
 
4.1%
Other values (44) 90218
38.0%

latitude
Real number (ℝ)

High correlation 

Distinct12439
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728455
Minimum40.50873
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:16.025456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum40.50873
5-th percentile40.64513
Q140.68942
median40.72273
Q340.76299
95-th percentile40.825653
Maximum40.91306
Range0.40433
Interquartile range (IQR)0.07357

Descriptive statistics

Standard deviation0.054755077
Coefficient of variation (CV)0.0013443937
Kurtosis0.10856551
Mean40.728455
Median Absolute Deviation (MAD)0.03658
Skewness0.23016938
Sum814569.1
Variance0.0029981185
MonotonicityNot monotonic
2025-11-05T15:18:16.196407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 8
 
< 0.1%
40.68634 8
 
< 0.1%
40.69414 8
 
< 0.1%
40.72232 8
 
< 0.1%
40.68683 7
 
< 0.1%
40.72607 7
 
< 0.1%
40.70587 7
 
< 0.1%
40.71801 7
 
< 0.1%
40.68084 7
 
< 0.1%
40.70969 6
 
< 0.1%
Other values (12429) 19927
99.6%
ValueCountFrequency (%)
40.50873 1
< 0.1%
40.52293 1
< 0.1%
40.53076 1
< 0.1%
40.53871 1
< 0.1%
40.53884 1
< 0.1%
40.53939 1
< 0.1%
40.54106 1
< 0.1%
40.54312 1
< 0.1%
40.5455 1
< 0.1%
40.54857 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.90527 1
< 0.1%
40.90391 1
< 0.1%
40.90356 1
< 0.1%
40.90329 1
< 0.1%
40.90281 1
< 0.1%
40.9026 1
< 0.1%
40.89981 1
< 0.1%
40.89811 1
< 0.1%
40.89756 1
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct10181
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.952125
Minimum-74.23914
Maximum-73.71795
Zeros0
Zeros (%)0.0%
Negative20000
Negative (%)100.0%
Memory size156.4 KiB
2025-11-05T15:18:16.347286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-74.23914
5-th percentile-74.004181
Q1-73.98303
median-73.95564
Q3-73.93638
95-th percentile-73.864896
Maximum-73.71795
Range0.52119
Interquartile range (IQR)0.04665

Descriptive statistics

Standard deviation0.046558783
Coefficient of variation (CV)-0.00062958006
Kurtosis4.9382429
Mean-73.952125
Median Absolute Deviation (MAD)0.024895
Skewness1.2551004
Sum-1479042.5
Variance0.0021677203
MonotonicityNot monotonic
2025-11-05T15:18:16.505844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.98589 10
 
0.1%
-73.95121 9
 
< 0.1%
-73.98043 9
 
< 0.1%
-73.95427 9
 
< 0.1%
-73.94829 9
 
< 0.1%
-73.95742 9
 
< 0.1%
-73.95725 9
 
< 0.1%
-73.94839 8
 
< 0.1%
-73.95107 8
 
< 0.1%
-73.95443 8
 
< 0.1%
Other values (10171) 19912
99.6%
ValueCountFrequency (%)
-74.23914 1
< 0.1%
-74.21238 1
< 0.1%
-74.20295 1
< 0.1%
-74.19826 1
< 0.1%
-74.19626 1
< 0.1%
-74.18259 1
< 0.1%
-74.17628 1
< 0.1%
-74.17388 1
< 0.1%
-74.17117 1
< 0.1%
-74.17065 1
< 0.1%
ValueCountFrequency (%)
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.72582 1
< 0.1%
-73.72716 1
< 0.1%
-73.72731 1
< 0.1%
-73.7274 1
< 0.1%
-73.72778 1
< 0.1%
-73.72817 1
< 0.1%
-73.72901 1
< 0.1%
-73.72928 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Entire home/apt
10384 
Private room
9172 
Shared room
 
444

Length

Max length15
Median length15
Mean length13.5354
Min length11

Characters and Unicode

Total characters270708
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 10384
51.9%
Private room 9172
45.9%
Shared room 444
 
2.2%

Length

2025-11-05T15:18:16.649841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-05T15:18:16.768921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
entire 10384
26.0%
home/apt 10384
26.0%
room 9616
24.0%
private 9172
22.9%
shared 444
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 30384
11.2%
t 29940
11.1%
o 29616
10.9%
r 29616
10.9%
20000
 
7.4%
a 20000
 
7.4%
m 20000
 
7.4%
i 19556
 
7.2%
h 10828
 
4.0%
E 10384
 
3.8%
Other values (7) 50384
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 270708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 30384
11.2%
t 29940
11.1%
o 29616
10.9%
r 29616
10.9%
20000
 
7.4%
a 20000
 
7.4%
m 20000
 
7.4%
i 19556
 
7.2%
h 10828
 
4.0%
E 10384
 
3.8%
Other values (7) 50384
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 270708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 30384
11.2%
t 29940
11.1%
o 29616
10.9%
r 29616
10.9%
20000
 
7.4%
a 20000
 
7.4%
m 20000
 
7.4%
i 19556
 
7.2%
h 10828
 
4.0%
E 10384
 
3.8%
Other values (7) 50384
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 270708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 30384
11.2%
t 29940
11.1%
o 29616
10.9%
r 29616
10.9%
20000
 
7.4%
a 20000
 
7.4%
m 20000
 
7.4%
i 19556
 
7.2%
h 10828
 
4.0%
E 10384
 
3.8%
Other values (7) 50384
18.6%

price
Real number (ℝ)

Distinct544
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.26905
Minimum0
Maximum10000
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:16.903569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q169
median105
Q3175
95-th percentile350
Maximum10000
Range10000
Interquartile range (IQR)106

Descriptive statistics

Standard deviation243.32561
Coefficient of variation (CV)1.5875717
Kurtosis538.29758
Mean153.26905
Median Absolute Deviation (MAD)45
Skewness18.30469
Sum3065381
Variance59207.352
MonotonicityNot monotonic
2025-11-05T15:18:17.047051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 856
 
4.3%
150 821
 
4.1%
50 636
 
3.2%
200 590
 
2.9%
75 570
 
2.9%
60 555
 
2.8%
80 531
 
2.7%
70 482
 
2.4%
120 471
 
2.4%
65 471
 
2.4%
Other values (534) 14017
70.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
10 6
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
16 3
 
< 0.1%
18 1
 
< 0.1%
19 3
 
< 0.1%
20 13
0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
8500 1
< 0.1%
7703 1
< 0.1%
7500 1
< 0.1%
6500 2
< 0.1%
5250 1
< 0.1%
5100 1
< 0.1%
5000 1
< 0.1%
4500 2
< 0.1%

minimum_nights
Real number (ℝ)

Skewed 

Distinct75
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9921
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:17.179367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation21.645449
Coefficient of variation (CV)3.0957007
Kurtosis1072.1676
Mean6.9921
Median Absolute Deviation (MAD)1
Skewness25.17997
Sum139842
Variance468.52546
MonotonicityNot monotonic
2025-11-05T15:18:17.321585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5248
26.2%
2 4796
24.0%
3 3309
16.5%
30 1540
 
7.7%
4 1328
 
6.6%
5 1208
 
6.0%
7 855
 
4.3%
6 307
 
1.5%
14 217
 
1.1%
10 183
 
0.9%
Other values (65) 1009
 
5.0%
ValueCountFrequency (%)
1 5248
26.2%
2 4796
24.0%
3 3309
16.5%
4 1328
 
6.6%
5 1208
 
6.0%
6 307
 
1.5%
7 855
 
4.3%
8 52
 
0.3%
9 34
 
0.2%
10 183
 
0.9%
ValueCountFrequency (%)
1250 1
 
< 0.1%
999 2
 
< 0.1%
480 1
 
< 0.1%
400 1
 
< 0.1%
370 1
 
< 0.1%
365 14
0.1%
364 1
 
< 0.1%
300 1
 
< 0.1%
299 1
 
< 0.1%
240 2
 
< 0.1%

number_of_reviews
Real number (ℝ)

High correlation  Zeros 

Distinct323
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2741
Minimum0
Maximum607
Zeros4123
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:17.458439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q323
95-th percentile114
Maximum607
Range607
Interquartile range (IQR)22

Descriptive statistics

Standard deviation44.927793
Coefficient of variation (CV)1.9303772
Kurtosis20.229807
Mean23.2741
Median Absolute Deviation (MAD)5
Skewness3.7613755
Sum465482
Variance2018.5066
MonotonicityNot monotonic
2025-11-05T15:18:17.599017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4123
20.6%
1 2131
 
10.7%
2 1394
 
7.0%
3 1033
 
5.2%
4 827
 
4.1%
5 631
 
3.2%
6 560
 
2.8%
7 513
 
2.6%
8 469
 
2.3%
9 400
 
2.0%
Other values (313) 7919
39.6%
ValueCountFrequency (%)
0 4123
20.6%
1 2131
10.7%
2 1394
 
7.0%
3 1033
 
5.2%
4 827
 
4.1%
5 631
 
3.2%
6 560
 
2.8%
7 513
 
2.6%
8 469
 
2.3%
9 400
 
2.0%
ValueCountFrequency (%)
607 1
< 0.1%
594 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
474 1
< 0.1%
467 1
< 0.1%
466 1
< 0.1%
459 1
< 0.1%
448 1
< 0.1%
447 1
< 0.1%

last_review
Date

Missing 

Distinct1507
Distinct (%)9.5%
Missing4123
Missing (%)20.6%
Memory size156.4 KiB
Minimum2011-05-12 00:00:00
Maximum2019-07-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-05T15:18:17.739320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:17.876368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

High correlation  Missing 

Distinct790
Distinct (%)5.0%
Missing4123
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean1.377446
Minimum0.01
Maximum27.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:18.001618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.72
Q32.01
95-th percentile4.67
Maximum27.95
Range27.94
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.6830056
Coefficient of variation (CV)1.2218306
Kurtosis11.9517
Mean1.377446
Median Absolute Deviation (MAD)0.62
Skewness2.4357993
Sum21869.71
Variance2.8325079
MonotonicityNot monotonic
2025-11-05T15:18:18.127979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 378
 
1.9%
1 378
 
1.9%
0.05 352
 
1.8%
0.03 334
 
1.7%
0.04 274
 
1.4%
0.08 263
 
1.3%
0.16 255
 
1.3%
0.09 246
 
1.2%
0.06 234
 
1.2%
0.11 222
 
1.1%
Other values (780) 12941
64.7%
(Missing) 4123
 
20.6%
ValueCountFrequency (%)
0.01 18
 
0.1%
0.02 378
1.9%
0.03 334
1.7%
0.04 274
1.4%
0.05 352
1.8%
0.06 234
1.2%
0.07 172
0.9%
0.08 263
1.3%
0.09 246
1.2%
0.1 198
1.0%
ValueCountFrequency (%)
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.22 1
< 0.1%
14 1
< 0.1%
13.45 1
< 0.1%
13.42 1
< 0.1%
13.24 1
< 0.1%
13.15 1
< 0.1%
Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.95545
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:18.266892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile14
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.433831
Coefficient of variation (CV)4.6630815
Kurtosis70.365352
Mean6.95545
Median Absolute Deviation (MAD)0
Skewness8.0961239
Sum139109
Variance1051.9534
MonotonicityNot monotonic
2025-11-05T15:18:18.413048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 13290
66.5%
2 2688
 
13.4%
3 1174
 
5.9%
4 570
 
2.9%
5 361
 
1.8%
6 226
 
1.1%
8 168
 
0.8%
7 166
 
0.8%
327 131
 
0.7%
9 103
 
0.5%
Other values (37) 1123
 
5.6%
ValueCountFrequency (%)
1 13290
66.5%
2 2688
 
13.4%
3 1174
 
5.9%
4 570
 
2.9%
5 361
 
1.8%
6 226
 
1.1%
7 166
 
0.8%
8 168
 
0.8%
9 103
 
0.5%
10 70
 
0.4%
ValueCountFrequency (%)
327 131
0.7%
232 89
0.4%
121 53
0.3%
103 36
 
0.2%
96 73
0.4%
91 34
 
0.2%
87 32
 
0.2%
65 27
 
0.1%
52 41
 
0.2%
50 16
 
0.1%

availability_365
Real number (ℝ)

Zeros 

Distinct366
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.9012
Minimum0
Maximum365
Zeros7176
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-11-05T15:18:18.559352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3229
95-th percentile359
Maximum365
Range365
Interquartile range (IQR)229

Descriptive statistics

Standard deviation131.76223
Coefficient of variation (CV)1.1670578
Kurtosis-1.0070922
Mean112.9012
Median Absolute Deviation (MAD)44
Skewness0.75940929
Sum2258024
Variance17361.284
MonotonicityNot monotonic
2025-11-05T15:18:18.725849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7176
35.9%
365 510
 
2.5%
364 225
 
1.1%
1 181
 
0.9%
5 136
 
0.7%
89 124
 
0.6%
179 121
 
0.6%
2 119
 
0.6%
3 118
 
0.6%
6 110
 
0.5%
Other values (356) 11180
55.9%
ValueCountFrequency (%)
0 7176
35.9%
1 181
 
0.9%
2 119
 
0.6%
3 118
 
0.6%
4 110
 
0.5%
5 136
 
0.7%
6 110
 
0.5%
7 98
 
0.5%
8 85
 
0.4%
9 85
 
0.4%
ValueCountFrequency (%)
365 510
2.5%
364 225
1.1%
363 98
 
0.5%
362 69
 
0.3%
361 48
 
0.2%
360 34
 
0.2%
359 52
 
0.3%
358 62
 
0.3%
357 37
 
0.2%
356 28
 
0.1%

Interactions

2025-11-05T15:18:11.635584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.021737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.089531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.099617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:05.204370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.416052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.427913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.486946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.516756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.511219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:11.744783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.145146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.192349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.210291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:05.309299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.520055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.538458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.589760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.619009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.621764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:11.841283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.243934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.287826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.312327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:05.406502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.616668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.640781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.688193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.712600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.742333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:11.952068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.355006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.400730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.421141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:05.729060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.732313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.750970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.799932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.826343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.867546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:12.046661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.456454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.502290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.528145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:05.826847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.829186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.852159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.897969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.918345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.974870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:12.140761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.563165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.598525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.647904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:05.923902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.925503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.946111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.996684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.006996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:11.078947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:12.238840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.672838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.698775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.768210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.022375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.029622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.043791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.104255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.107370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:11.204406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:12.337321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.777617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.794504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.880960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.124612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.131922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.147596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.208303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.214429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:11.324921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:12.441219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.876089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.889793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:04.982908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.217565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.223761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.265797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.302761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.309562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:11.425896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:12.558524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:02.980766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:03.991885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:05.092426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:06.316679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:07.316913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:08.378657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:09.412707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:10.409038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-05T15:18:11.527035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-11-05T15:18:18.840136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsneighbourhood_groupnumber_of_reviewspricereviews_per_monthroom_type
availability_3651.0000.4070.1730.165-0.0100.0610.0810.0760.2380.0920.3880.089
calculated_host_listings_count0.4071.0000.1450.1330.0020.0630.0640.0860.057-0.1010.1440.095
host_id0.1730.1451.0000.5580.0420.107-0.1390.098-0.118-0.0740.2760.092
id0.1650.1330.5581.000-0.0010.067-0.0680.059-0.302-0.0270.3690.071
latitude-0.0100.0020.042-0.0011.0000.0400.0170.538-0.0380.139-0.0270.112
longitude0.0610.0630.1070.0670.0401.000-0.1210.6490.081-0.4370.1170.154
minimum_nights0.0810.064-0.139-0.0680.017-0.1211.0000.000-0.1590.103-0.2880.020
neighbourhood_group0.0760.0860.0980.0590.5380.6490.0001.0000.0270.0100.0660.125
number_of_reviews0.2380.057-0.118-0.302-0.0380.081-0.1590.0271.000-0.0510.7020.000
price0.092-0.101-0.074-0.0270.139-0.4370.1030.010-0.0511.000-0.0190.023
reviews_per_month0.3880.1440.2760.369-0.0270.117-0.2880.0660.702-0.0191.0000.017
room_type0.0890.0950.0920.0710.1120.1540.0200.1250.0000.0230.0171.000

Missing values

2025-11-05T15:18:12.711299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-05T15:18:13.007187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-05T15:18:13.227378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
09138664Private Lg Room 15 min to Manhattan47594947IrisQueensSunnyside40.74271-73.92493Private room74262019-05-260.1315
131444015TIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC8523790JohlexManhattanHell's Kitchen40.76682-73.98878Entire home/apt17030NaNNaN1188
28741020Voted #1 Location Quintessential 1BR W Village Apt45854238JohnManhattanWest Village40.73631-74.00611Entire home/apt2453512018-09-191.1210
334602077Spacious 1 bedroom apartment 15min from Manhattan261055465ReganQueensAstoria40.76424-73.92351Entire home/apt125312019-05-240.65113
423203149Big beautiful bedroom in huge Bushwick apartment143460MeganBrooklynBushwick40.69839-73.92044Private room65282019-06-230.5228
54402805LRG 2br BKLYN APT CLOSE TO TRAINS AND PARK22807362JennyBrooklynProspect-Lefferts Gardens40.66025-73.96270Entire home/apt120332018-08-280.05116
630070126✩Prime Renovated 1/1 Apartment in Upper East Side✩4968673SeanManhattanUpper East Side40.76831-73.95929Entire home/apt200522019-05-260.68171
734231172Fully renovated brick house floor in Brooklyn59642348KevinBrooklynSunset Park40.64550-74.01262Entire home/apt95192019-07-089.001106
85856760Renovated 1BR in exciting, convenient area29408349ChadManhattanChinatown40.71490-73.99976Entire home/apt179572017-04-180.1410
97929441Beautiful Loft w/ Waterfront View!1453898AnthonyBrooklynWilliamsburg40.71268-73.96676Private room10522322019-06-195.00364
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
199905192459Quiet Room in 4BR UWS Brownstone10677483GregManhattanUpper West Side40.80173-73.96625Private room7010NaNNaN10
199911327940Huge Gorgeous Park View Apartment!3290436HadarBrooklynFlatbush40.65335-73.96257Entire home/apt1203132016-08-270.282327
1999223612681Shared Room 1 Stop from Manhattan on the F Train55724558TaylorQueensLong Island City40.76006-73.94080Private room55422019-06-010.65589
1999334485745Midtown Manhattan Stunner - Private room261632622RoyaltonManhattanTheater District40.75491-73.98507Private room100132019-06-163.009318
1999425616250Stylish, spacious, private 1BR apt in Ditmas Park125396920AdamBrooklynFlatbush40.64314-73.95705Entire home/apt753102019-01-030.8410
199957094539Tranquil haven in bubbly Brooklyn2052211AdrianaBrooklynWindsor Terrace40.65360-73.97546Entire home/apt1431422016-08-270.04110
199964424261Large 1 BR with backyard on UWS3447311SarahManhattanUpper West Side40.80188-73.96808Entire home/apt2002222019-05-210.5010
199974545882Amazing studio/Loft with a backyard23569951KavehManhattanUpper East Side40.78110-73.94567Entire home/apt2203282019-05-230.501293
1999826518547U2 comfortable double bed sleeps 2 guests295128Carol GloriaBronxClason Point40.81225-73.85502Private room80142019-07-011.487365
1999933631782Private Bedroom in Williamsburg Apt!8569221AndiBrooklynWilliamsburg40.71829-73.95819Private room109332019-04-281.07297